Abd. Rahman As-syakur

Enhanced Built-Up and Bareness Index (EBBI) for Mapping Built-Up and Bare Land in an Urban Area

Remotely sensed imagery is a type of data that is compatible with the monitoring and mapping of changes in built-up and bare land within urban areas as the impacts of population growth and urbanisation increase. The application of currently available remote sensing indices, however, has some limitations with respect to distinguishing built-up and bare land in urban areas. In this study, a new index for transforming remote sensing data for mapping built-up and bare land areas is proposed. The Enhanced Built-Up and Bareness Index (EBBI) is able to map built-up and bare land areas using a single calculation. The EBBI is the first built-up and bare land index that applies near infrared (NIR), short wave infrared (SWIR), and thermal infrared (TIR) channels simultaneously. This new index was applied to distinguish built-up and bare land areas in Denpasar (Bali, Indonesia) and had a high accuracy level when compared to existing indices. The EBBI was more effective at discriminating built-up and bare land areas and at increasing the accuracy of the built-up density percentage than five other indices.

Validation of TRMM Precipitation Radar satellite data over Indonesian region

Research has been conducted to validate monthly and seasonal rain rates derived from the Tropical Rainfall Measuring Mission Precipitation Radar (PR) using rain gauge data analysis from 2004 to 2008. The study area employed 20 gauges across Indonesia to monitor three Indonesian regional rainfall types. The relationship of PR and rain gauge data statistical analysis included the linear correlation coefficient, the mean bias error (MBE), and the root mean square error (RMSE). Data validation was conducted with point-by-point analysis and spatial average analysis. The general results of point-by-point analysis indicated satellite data values of medium correlation, while values of MBE and RMSE tended to indicate underestimations with high square errors. The spatial average analysis indicated the PR data values are lower than gauge values of monsoonal and semi-monsoonal type rainfall, while anti-monsoonal type rainfall was overestimated. The validation analysis showed very good correlation with the gauge data of monsoonal type rainfall, high correlation for anti-monsoonal type rainfall, but medium correlation for semi-monsoonal type rainfall. In general, the statistical error level of monthly seasonal monsoonal type conditions is more stable compared to other rainfall types. Unstable correlations were observed in months of high rainfall for semi-monsoonal and anti-monsoonal type rainfall.

Comparison of TRMM multisatellite precipitation analysis (TMPA) products and daily-monthly gauge data over Bali

Research has been conducted to compare daily, monthly and seasonal rain rates derived from Tropical Rainfall Measuring Mission (TRMM) multisatellite precipitation analysis (TMPA) using rain gauge analysis from 1998 to 2002. Three rain gauges in the Bali islands were employed. Statistical analysis was used to analyse the relationship of the TMPA product with the rain gauge data. Resulting statistical measures consisted of the linear correlation coefficient (r), the mean bias error (MBE), the root mean square error (RMSE) and the mean absolute error (MAE). The results of these analyses indicate that satellite data have lower values than the gauge estimation values. The validation analysis showed a very good relationship with the gauge data on monthly timescales. However, a poor relationship was found between the gauge data and the daily data analysis from the TMPA. The 3B42 and 3B43 products showed the same levels of relationship during the wet season and dry season. The correlation in the dry season was better than during the wet season. Statistical error levels during the wet season were better than in the dry season. The 3B43 showed slight improvement in these values when compared with the 3B42 (both the random error measurement and the scatter of the estimates were reduced). In general, the data from TMPA are potentially usable to replace rain gauge data, especially to replace the monthly data, if inconsistencies and errors are taken into account.

Indonesian rainfall variability observation using TRMM multi-satellite data

It is important to understand the characteristics of Indonesian rainfall within the world’s climate system. The large rainfall in the Indonesian archipelago plays an essential role as a central atmospheric heat source of the Earth’s climate system throughout the year. Monthly rainfall satellite data, measured by the Tropical Rainfall Measuring Mission (TRMM) 3B43 over the course of 13 years, were employed to analyse monthly means, total means, maximum and minimum variability, standard deviation, and the trends analysis of Indonesian rainfall variability. The rainfall estimated from satellite data was then compared to the rain gauge data over the Indonesian region to determine the accuracy level. The results show that oceans, islands, monsoons, and topography clearly affect the spatial patterns of Indonesian rainfall. Most high-rainfall events in Indonesia peak during the December–January–February (DJF) season and the lowest rainfall events occur during the June–July–August (JJA) season. Those conditions are associated and generated with the northwest and southeast monsoon patterns. High fluctuations between maximum and minimum monthly rainfall data of over 400 mm month−1 occur over Jawa (Java) Island, the Jawa Sea, and southern Sulawesi Island. A high annual and monthly rainfall typically occurs throughout Indonesia over island areas. The trend analysis shows an increasing trend in rainfall from 1998 to 2010 in Kalimantan, Jawa, Sumatra, and Papua. Decreasing rainfall trends occur along the west and south coast of Sumatra, eastern Jawa, southern Sulawesi, Maluku Islands, western Papua, and Bali Island.

Shallow-Water Benthic Identification Using Multispectral Satellite Imagery: Investigation on the Effects of Improving Noise Correction Method and Spectral Cover

Lyzengas method is used widely for radiative transfer analysis because of its simplicity of application to cases of shallow-water coral reef ecosystems with limited information of water properties. WorldView-2 imagery has been used previously to study bottom-type identification in shallow-water coral reef habitats. However, this is the first time WorldView-2 imagery has been applied to bottom-type identification using Lyzengas method. This research applied both of Lyzengas methods: the original from 1981 and the one from 2006 with improved noise correction that uses the near-infrared (NIR) band. The objectives of this study are to examine whether the utilization of NIR bands in the correction of atmospheric and sea-surface scattering improves the accuracy of bottom classification, and whether increasing the number of visible bands also improves accuracy. Firstly, it has been determined that the improved 2006 correction method, which uses NIR bands, is only more accurate than the original 1981 correction method in the case of three visible bands. When applying six bands, the accuracy of the 1981 correction method is

Application of ALOS/PALSAR to estimate carbon dioxide transfer velocities compared with satellite wind speed data 2007–2008

Gas exchange contributes to the mitigation of an anthropogenic greenhouse effect through absorption of excess atmospheric CO2 by the oceans. The gas transfer velocity requires computation in order to describe gas exchange and interaction. The absolute calibration of the relationship between air and sea CO2 transfer velocity and wind speed (U 10) has been under debate for a long time because the global averages of the CO2 exchange coefficients, deduced from many experimental data relationships, disagreed with each other. In this research, the CO2 transfer velocity ( ) was derived from satellite wind speeds that were computed using three different parameterizations (k–U) and was then compared with the derived from Phased Array type L-band Synthetic Aperture Radar (PALSAR) (k P). To obtain the transfer velocity of CO2 between the atmosphere and ocean using PALSAR in the Bali Sea area close to the Indian Ocean, daily sea surface temperature, wind friction velocity (U*) from PALSAR images and oceanography data were collected and analysed. Additionally, values were computed from the QuikSCAT satellite wind speed and compared to three different k–U relationships suggested by different authors. The three algorithms of parameterization were used with QuikSCAT daily data and produced a value that was close to that of derived from PALSAR (k P).

Indeks Kesesuaian Wisata di Pantai Pasir Putih, Kabupaten Karangasem

The study of tourist suitability index (IKW) was conducted in Pasir Putih Beach, Bugbug Village, Karangasem Subdistrict, Karangasem district. Data was collected for 1 (one) month in January 2017 at 4 observation stations. Sources of primary data are obtained from observations and interviews with tourists and secondary data obtained from literature that related to agencies in Pasir Putih beach. Purposive sampling is used as a method for data collection. The data collected are incluted: coastal type, beach width, water depth, coastal slope, water base material, water flow velocity, waters brightness, coastal closure, harmful biota, and freshwater availability. The analysis of IKW for recreation beach category refers to the matrix of tourism and tourism conformity classification. The limiting factor of IKW in Pasir Putih Beach is coastal land cover and wide beach. IKW value for tourism activities in the beach recreation category at Pasir Putih Beach at station I of 94.23%, II of 96.79%, III by 85.89%, and VI of 81.41%. All stations are categorized as suitable (SS) for coastal tourism activities. Zone utilization coastal tourist area for swimming and sunbathing done at station I and II. Zone utilization area for recreation of a walk are station I, II, III, and IV. Station IV at south area including zone of sacred area.

Hubungan Hasil Tangkapan Ikan Tuna Selama Februari-Maret 2016 dengan Konsentrasi Klorofil-a dan SPL dari Data Penginderaan Jauh Di Perairan Selatan Jawa – Bali

Indonesia waters are fertile waters marked by the existence of Regional Fisheries Management (WPP), one of them is WPP 573 in Southern Java. One of the biggest fish catch sectors in Indonesia is tuna fisheries. Distribution of tuna in Indonesia waters affected by Sea Surface Temperature (SST) and chlorophyll-a. The aims of this study is to analyze the distribution of chlorophyll-a and SST in the water of South Java - Bali using AQUA MODIS satellite through data in-situ, to know the accuracy of SST Ground Truth and SST imagery satellite and to find the relationship between chlorophyll-a and SST with catches tuna in the water of South Java - Bali. SST data and chlorophyll-a were obtained through remote sensing technology and the catches of tuna was obtained by in-situ. The methods used were polynomial regression analysis, regression linear analysis and correlation analysis to determine the relationship of these parameters, found strong correlation between SST Ground Truth and SST imagery satellite R= 0,61. The distribution of oceanographic parameters (SST and chlorophyll-a) in the water of South Java - Bali seemed volatile and these condition also seemed less affected catches of tuna. Found low inverse correlation SST with catches of tuna amounted to R = -0.34 and low correlation with the chlorophyll-a with catches of tuna amounted to R = 0.28.

Spatio-Temporal Variations of Rainfall and SST Anomaly over Indonesia during ENSO Modoki Event in 2010

Remote sensing application is one of the best data to observing spatial and temporal situation on earth surface. Application of Tropical Rainfall Measuring Mission (TRMM) and MODIS (Moderate Resolution Imaging Spectro-radiometer) are use for spatial and temporal analysis of rainfall and sea surface temperature (SST) anomaly over Indonesia in 2010. Spatial and temporal rainfall and SST anomaly data is important, especially during El Nino–Southern Oscillation (ENSO) Modoki events, because has wide effect of social and economy in Indonesia. Monthly rainfall data measured by the TRMM 3B43 over the course of 13 years and Monthly SST collected by the MODIS was employed to analyze anomaly of rainfall and SST over Indonesia during ENSO Modoki Event in 2010. In spatio-temporal seen confirmed during ENSO Modoki in 2010 indicate an anomaly has occurred on rainfall and SST over Indonesia. The result shows that increasing of rainfall anomaly begin from April in Nusa Tenggara archipelago and eastern of Java and finish in November in those region. Meanwhile, analysis result of MODIS satellite data for SST anomaly is shown at the beginning of 2010, SST anomaly begin occurred in western of Indonesia and the biggest was happen in southern of Indonesia at August to September and finish at November. Spatio-temporal analysis TRMM dan MODIS data shows that increasing of SST anomaly could affect increasing of rainfall anomaly in those same regions excepted in January to March.